Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe effect of sample size on the statistical test power
تقدير أثر حجم العينة في قوة الاختبار الإحصائي
1912
8 48
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
هدف البحث إلى تقدير أثر حجم العينة على قوة اختبار (t) لعينة واحدة، و لعينتين مترابطتين، و كذلك لعينتين مستقلتين، و على قوة اختبار تحليل التباين الأحادي (F) للمقارنة بين المتوسطات الحسابية. و قد تم استخدام المنهج الوصفي، و تم استخدام أحجام مختلفة من العينات ( 300 ) مشاىدة إحصائية، حيث تم توليدها باستخدام برنامج (PASS 14) و روعي أن تتحقق في هذه البيانات مجموعة الافتراضات اللازمة لاختباري (F) و (t) سواء فيما بتعلق بالاختيار العشوائي، مستوى القياس الفئوي، التوزيع الاعتدالي، تجانس التباين.
The research aims to estimate the effect of sample size on the statistical test power (t) for one sample, two interrelated samples, two independent samples, and on the statistical test power of one-way analysis of variance test (F) to compare the averages. The descriptive method was used, and different sizes of samples (300) items, where it was generated using the program (PASS 14), and taken into account to be realized in this data the set of assumptions needed to make test (t) and (F), with respect to random testing, categorical level of measurement, normal distribution, and equinoctial variance.
References used
Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York: Academic Press
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum
Hamadneh, Iyad Mohammed (2015). Statistical Power and Effect Size in Educational and Psychological Research Published in Journal of AL-MANARAH for Research and Studies, Research on Humanities and Social Sciences. Vol.5, No.20
rate research
Read More
Participation in inter-laboratory comparison programs is an important means of laboratory quality
control and assessing laboratory performance, and these programs can be used by customers or
regulatory bodies for the selection of qualified laborato
ries.
This research describes how to use inter-comparison tests and how to statistically analyse the test
results.
This research has a practical study of assessing laboratories performance in laboratories of the Syrian
textile firms by distributing samples simultaneously to participating laboratories for testing. After
collecting test results, the researcher used scientific methods to handle data to identify the weak points
in laboratories performance and provide them the Feedback and technical advice to Assistance the lab
to defining the measurement problems and evaluating of test methods and instrumentation , and could
introduce some suggestions and recommendations to overcome.
The research aims to develop some formulas of sample size and characterization and comparison among themselves to determine the best formula of formulas to calculate the sample size and the creation of a modified reflected well on the sample size, in
addition to specifying individual gratification I and II for the relevant formulas and mathematical equations can predict the sample size, however the size of the community. The researcher through the study the following results:
The results were identical to the formula related to the size and the sample size when consolidation requirements.
Sample size did not increase with increasing size of the moral community at first gratification.
No moral differences between sample volume according to the size of the community when individual gratification.
Moral differences exist between sample size and average total inspection according to the size of the community when individual gratification.
We got a mathematical models of the relationship between size and the sample size and the size of the community and the average total inspection.
We have developed a comprehensive table gives sample size corresponding to the size of the community can be accessible to researchers to take advantage of it and apply the formulas as long as it originally relied upon certain conditions.
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting th
e ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.
These papers aim to study the estimation of the simple linear regression equation
coefficients using the least square method at different sample sizes and different sampling
methods. And so on, the main goal of this research is to try to determine
the optimum size
and the best sampling method for these coefficients. We used experimental data for a
population consist of 2000 students from different schools all over the country. We had
changed the sample size each time and calculate the coefficients and then compare these
coefficients for different sample sizes with their coefficients of the real population; and the
results have been shown that the estimation of the linear regression equation coefficients
are close from the real values of the coefficients of the regression line equation for the
population when the sample size closes the value (325). As it turns out that the Stratified
random sampling with proportional distribution with class sizes gives the best and most
accurate results to estimate linear regression equation with least square method.
This research aims to show the importance of ensuring the
competence of all who operate specific equipment, perform tests
and/or calibrations, evaluate results, and sign test reports and
calibration certificates.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
